Exchange rate and sentiment: is there a connection?

Before we can start with our machine learning model we need to understand the relationship between the two variables, therefore we should calculate covariance. This measures the direction of a relationship between the two variables.

Loading libraries to be used

library(tidyverse)
library(writexl)
library(plyr)
library(lubridate)
library(plotly)
library(dplyr)
library(corrplot)
library(formatR)

First step: creating a dataframe from the csv

btc_exchange_rate_history <- read.csv("D:/Suli/Szakdolgozat1/development_n_stuff/aggregated_data.csv") %>%
    select(-X) %>%
    mutate(created_at = as_date(created_at))



btc_usd_tweets_combined <- read.csv("D:/Suli/Szakdolgozat1/data_to_be_cleaned/coin_Bitcoin.csv") %>%
    mutate(Date = as_date(Date)) %>%
    mutate(year = year(Date), month = month(Date), day = day(Date)) %>%
    mutate(Date = make_date(year, month, day)) %>%
    mutate(xchange_rate_change = Close - Open) %>%
    filter(Date >= "2019-01-01" & Date <= "2022-03-30") %>%
    inner_join(btc_exchange_rate_history, by = c(Date = "created_at")) %>%
    select(c(-year, -month, -day))

Second step: plotting exchange rate change and average sentiment for further insights, taking a look at distribution

btc_sent_lineplot <- plot_ly(data = btc_usd_tweets_combined, x = ~Date, y = ~xchange_rate_change,
    name = "BTC change", type = "scatter", mode = "lines", color = "red") %>%
    add_trace(data = btc_usd_tweets_combined, x = ~Date, y = ~daily_avg_sent, yaxis = "y2",
        name = "Avg. sentiment", mode = "lines", color = "blue") %>%
    layout(title = "Bitcoin exchange rate change compared to previous day's closing rate",
        margin = list(t = 150), legend = list(x = 1.1), paper_bgcolor = "rgb(255, 255, 255)",
        plot_bgcolor = "rgb(255, 255, 255)", xaxis = list(title = "Date", range = list("2019-01-01 00:00:00",
            "2019-12-31 23:59:59"), rangeslider = list(type = "date", visible = T),
            list(dtickrange = list(NULL, 1000), value = "%H:%M:%S.%L ms"), list(dtickrange = list(1000,
                60000), value = "%H:%M:%S s"), list(dtickrange = list(60000, 3600000),
                value = "%H:%M m"), list(dtickrange = list(3600000, 86400000), value = "%H:%M h"),
            list(dtickrange = list(86400000, 604800000), value = "%e. %b d"), list(dtickrange = list(604800000,
                "M1"), value = "%e. %b w"), list(dtickrange = list("M1", "M12"),
                value = "%b '%y M"), list(dtickrange = list("M12", NULL), value = "%Y Y"),
            rangeselector = list(buttons = list(list(count = 1, label = "1M", step = "month",
                stepmode = "backward"), list(count = 6, label = "6M", step = "month",
                stepmode = "backward"), list(count = 1, label = "1Y", step = "year",
                stepmode = "backward"), list(count = 1, label = "YTD", step = "year",
                stepmode = "todate"), list(step = "all", label = "ALL"))), list(dtick = "M1",
                tickformat = "%b\n%Y", ticklabelmode = "period")), yaxis = list(title = "BTC exchange rate change",
            range = c(min(btc_usd_tweets_combined$xchange_rate_change), max(btc_usd_tweets_combined$xchange_rate_change)),
            gridcolor = "rgb(255,255,255)", showgrid = TRUE, showline = FALSE, showticklabels = TRUE,
            tickcolor = "rgb(140, 140, 140)", ticks = "outside", zeroline = FALSE),
        yaxis2 = list(title = "Daily average sentiment", overlaying = "y", side = "right",
            range = c(min(btc_usd_tweets_combined$daily_avg_sent), max(btc_usd_tweets_combined$daily_avg_sent))))


btc_sent_lineplot
plot(density(btc_exchange_rate_history$daily_avg_sent), type="n", main = "Distribution of average daily sentiment")
polygon(density(btc_exchange_rate_history$daily_avg_sent), col="red", border="gray")

plot(density(btc_usd_tweets_combined$xchange_rate_change), type="n", main = "Distribution of Bitcoin exchange rate change")
polygon(density(btc_usd_tweets_combined$xchange_rate_change), col="red", border="gray")

These seem like pretty standard normal distributions, so we can use Pearson Correlation Coefficient calculation later on.

Third step: plotting the data on a scatterplot

btc_sent_scatterplot <- plot_ly(data = btc_usd_tweets_combined, y = ~daily_avg_sent,
    x = ~xchange_rate_change, marker = list(size = 4, color = "rgba(255, 182, 193, .9)",
        line = list(color = "rgba(152, 0, 0, .8)", width = 1))) %>%
    layout(yaxis = list(title = "Daily average sentiment"), xaxis = list(title = "BTC exchange rate change"))

btc_sent_scatterplot

The plot shows that there is no distinguishable relationship between these variables, but I still wanted to do the covariance and correlation calculation to get the mathematical results.

Fourth step: calculate covariance and correlation

btc_cov <- cov(btc_usd_tweets_combined$daily_avg_sent, btc_usd_tweets_combined$xchange_rate_change,
    method = "pearson")

btc_cov
## [1] 3.301177

A positive covariance means that the two variables tend to increase or decrease together. Correlation helps us analyze the effect of changes made in one variable over the other variable of the dataset. Now that we know this, we should calculate the strength of the relationship between two, numerically measured, continuous variables.

btc_cor <- cor(btc_usd_tweets_combined$daily_avg_sent, btc_usd_tweets_combined$xchange_rate_change,
    method = "pearson")

btc_cor
## [1] 0.1162632

One of the most common ways to quantify a relationship between two variables is to use the Pearson correlation coefficient, which is a measure of the linear association between two variables.

It always takes on a value between -1 and 1 where:

  • -1 indicates a perfectly negative linear correlation between two variables
  • 0 indicates no linear correlation between two variables
  • 1 indicates a perfectly positive linear correlation between two variables

Often denoted as r, this number helps us understand the strength of the relationship between two variables. The closer r is to zero, the weaker the relationship between the two variables.

A weak correlation indicates that there is minimal relationship between the variables.

After reading some scientific papers I concluded that continuing down this path would bear no plausible outcome, so I decided to look at some other trend measures that seem promising.

Fifth step: checking out GoogleTrends

Downloaded the data for Google search popularity of Bitcoin through Trendecon and Google Trends API. Because of the limitations of standard Google Trends data I had to use the Trendecon package’s rts_gtrends_mwd function to build a consistent daily time series.

Construct a robust and consistent daily Time Series from Google Trends data. Daily, weekly and monthly Data is downloaded and consistently aggregated, using the Chow-Lin methodology.

btc_ggl_trends <- read.csv("btc_google_trends.csv")

head(btc_ggl_trends)
##   X       time     value
## 1 1 2019-01-01  1.054060
## 2 2 2019-01-02 17.290539
## 3 3 2019-01-03 19.200650
## 4 4 2019-01-04 15.380214
## 5 5 2019-01-05  7.739413
## 6 6 2019-01-06  7.739342

Checking distribution:

plot(density(btc_ggl_trends$value), type="n", main = "Distribution of Google Trends data")
polygon(density(btc_ggl_trends$value), col="red", border="gray")

//TODO: FINISH THIS